Refactor: Clean up LR handling logic in LoRA GUI

This commit refactors the learning rate (LR) handling in `kohya_gui/lora_gui.py` for LoRA training.

The previous fix for LR misinterpretation involved commenting out a line. This commit completes the cleanup by:
- Removing the `do_not_set_learning_rate` variable and its associated conditional logic, which became redundant.
- Renaming the float-converted `learning_rate` to `learning_rate_float` for clarity.
- Ensuring that `learning_rate_float` and the float-converted `unet_lr_float` are consistently used when preparing the `config_toml_data` for the training script.

This makes the code cleaner and the intent of always passing the main learning rate (along with specific TE/UNet LRs) more direct. The functional behavior of the LR fix remains the same.

Author: google-labs-jules[bot]

kohya_gui/lora_gui.py

@@ -1421,27 +1421,22 @@ def train_model(
         text_encoder_lr_list = [float(text_encoder_lr), float(text_encoder_lr)]
 
     # Convert learning rates to float once and store the result for re-use
-    learning_rate = float(learning_rate) if learning_rate is not None else 0.0
+    learning_rate_float = float(learning_rate) if learning_rate is not None else 0.0
     text_encoder_lr_float = (
         float(text_encoder_lr) if text_encoder_lr is not None else 0.0
     )
     unet_lr_float = float(unet_lr) if unet_lr is not None else 0.0
 
     # Determine the training configuration based on learning rate values
     # Sets flags for training specific components based on the provided learning rates.
-    if float(learning_rate) == unet_lr_float == text_encoder_lr_float == 0:
+    if learning_rate_float == unet_lr_float == text_encoder_lr_float == 0:
         output_message(msg="Please input learning rate values.", headless=headless)
         return TRAIN_BUTTON_VISIBLE
     # Flag to train text encoder only if its learning rate is non-zero and unet's is zero.
     network_train_text_encoder_only = text_encoder_lr_float != 0 and unet_lr_float == 0
     # Flag to train unet only if its learning rate is non-zero and text encoder's is zero.
     network_train_unet_only = text_encoder_lr_float == 0 and unet_lr_float != 0
 
-    do_not_set_learning_rate = False  # Initialize with a default value
-    if text_encoder_lr_float != 0 or unet_lr_float != 0:
-        log.info("Learning rate won't be used for training because text_encoder_lr or unet_lr is set.")
-        # do_not_set_learning_rate = True # This line is now commented out
-
     clip_l_value = None
     if sd3_checkbox:
         # print("Setting clip_l_value to sd3_clip_l")
@@ -1519,7 +1514,7 @@ def train_model(
         "ip_noise_gamma": ip_noise_gamma if ip_noise_gamma != 0 else None,
         "ip_noise_gamma_random_strength": ip_noise_gamma_random_strength,
         "keep_tokens": int(keep_tokens),
-        "learning_rate": None if do_not_set_learning_rate else learning_rate,
+        "learning_rate": learning_rate_float,
         "logging_dir": logging_dir,
         "log_config": log_config,
         "log_tracker_name": log_tracker_name,
@@ -1640,7 +1635,7 @@ def train_model(
         "train_batch_size": train_batch_size,
         "train_data_dir": train_data_dir,
         "training_comment": training_comment,
-        "unet_lr": unet_lr if unet_lr != 0 else None,
+        "unet_lr": unet_lr_float if unet_lr_float != 0.0 else None,
         "log_with": log_with,
         "v2": v2,
         "v_parameterization": v_parameterization,